Bibliographic record
Abstract
This thesis proposes a new method of seat cushion shape morphing. The proposed method is a flexible patch-based approach allowing localized morphing for areas of the back or neck that are often lack of support. The focus of this work is on the lumbar region to demonstrate cushion morphing to provide the needed support. The thesis introduces Nitinol, a shape memory alloy, and describes the process of making Nitinol springs as the actuators to bend a flexible panel behind the lumbar area for cushion morphing. An approximation theory has been developed to relate the human weight to the required stiffness for spring design and fabrication. The spring control issues have been studied to address, i) the blasting current for obtaining desired shape deformation, and ii) the holding current for maintaining deformation. Settling time and spring temperature have been studied to culminate in a final design. At last, the springs have been integrated with a cushion support panel in the lumbar area for a commercial chair. Along with a pressure sensing cover, a morphing chair backrest has been developed under a closed-loop control. It has been successfully tested that this chair can automatically adjust the lumbar profile according to an allowable sitting pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".